Design of a Speech Recognition System Based on Acoustically Derived Segmental Units

View/ Open
Author(s)
Bacchiani, M
Ostendorf, M
Sagisaka, Y
Paliwal, K
Griffith University Author(s)
Year published
1996
Metadata
Show full item recordAbstract
The design of speech recognition system based on acoustically- derived, segmental units can be divided in three steps: unit design, lexicon building and pronunciation modeling. We formulate an iterative unit design procedure which consistently uses a maximum likelihood (ML) objective in successive application of resegmentation and model re-estimation. The lexicon building allows multi-word entries in the lexicon but restricts the number of these entries in order to avoid a too costly search. Selected multi-word lexical entries are those with high frequency (such as function words) and those which consistently exhibit ...
View more >The design of speech recognition system based on acoustically- derived, segmental units can be divided in three steps: unit design, lexicon building and pronunciation modeling. We formulate an iterative unit design procedure which consistently uses a maximum likelihood (ML) objective in successive application of resegmentation and model re-estimation. The lexicon building allows multi-word entries in the lexicon but restricts the number of these entries in order to avoid a too costly search. Selected multi-word lexical entries are those with high frequency (such as function words) and those which consistently exhibit cross-word phone assimilation. The stochastic pronunciation model represents the likelihood of a particular acoustic segment sequence given the phonetic baseform of a lexical item, where the sequence of baseform phones are treated as a Markov state sequence and each state can emit multiple segments.
View less >
View more >The design of speech recognition system based on acoustically- derived, segmental units can be divided in three steps: unit design, lexicon building and pronunciation modeling. We formulate an iterative unit design procedure which consistently uses a maximum likelihood (ML) objective in successive application of resegmentation and model re-estimation. The lexicon building allows multi-word entries in the lexicon but restricts the number of these entries in order to avoid a too costly search. Selected multi-word lexical entries are those with high frequency (such as function words) and those which consistently exhibit cross-word phone assimilation. The stochastic pronunciation model represents the likelihood of a particular acoustic segment sequence given the phonetic baseform of a lexical item, where the sequence of baseform phones are treated as a Markov state sequence and each state can emit multiple segments.
View less >
Conference Title
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume
1
Copyright Statement
© 1996 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Subject
Forestry sciences